Empowering IoT Resilience: Hybrid Deep Learning Techniques for Enhanced Security
<p dir="ltr">The Internet of Things (IoT) has dramatically changed human context with the environment, ensuring productivity, comfort, and quality of life through a variety of services and applications. Nevertheless, the rapid growth of IoT devices has introduced significant security...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , , |
| منشور في: |
2024
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513550794358784 |
|---|---|
| author | Muhammad Maaz (5600600) |
| author2 | Ghufran Ahmed (6298196) Ahmad Sami Al-Shamayleh (17122985) Adnan Akhunzada (20151648) Shahbaz Siddiqui (6296942) Abdulla Hussein Al-Ghushami (20748818) |
| author2_role | author author author author author |
| author_facet | Muhammad Maaz (5600600) Ghufran Ahmed (6298196) Ahmad Sami Al-Shamayleh (17122985) Adnan Akhunzada (20151648) Shahbaz Siddiqui (6296942) Abdulla Hussein Al-Ghushami (20748818) |
| author_role | author |
| dc.creator.none.fl_str_mv | Muhammad Maaz (5600600) Ghufran Ahmed (6298196) Ahmad Sami Al-Shamayleh (17122985) Adnan Akhunzada (20151648) Shahbaz Siddiqui (6296942) Abdulla Hussein Al-Ghushami (20748818) |
| dc.date.none.fl_str_mv | 2024-10-16T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2024.3482005 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Empowering_IoT_Resilience_Hybrid_Deep_Learning_Techniques_for_Enhanced_Security/28442045 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Cybersecurity and privacy Data management and data science Distributed computing and systems software Machine learning IoT machine learning (ML) deep learning (DL) cybersecurity DDOS injection attacks backdoor botnet |
| dc.title.none.fl_str_mv | Empowering IoT Resilience: Hybrid Deep Learning Techniques for Enhanced Security |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The Internet of Things (IoT) has dramatically changed human context with the environment, ensuring productivity, comfort, and quality of life through a variety of services and applications. Nevertheless, the rapid growth of IoT devices has introduced significant security concerns like device vulnerabilities, unauthorized access, and potential data breaches.This article deals with an immediate call to empower IoT resilience against a wide range of sophisticated and prevalent cybersecurity threats. We developed two novel hybrid deep learning mechanisms, CNN-GRU (Convolutional Gated Recurrent Neural Networks) and CNN-LSTM (Convolutional Long Short-Term Memory Neural Networks), and extensively evaluated their performance on the state-of-the-art Kitsune and TON-IoT publicly available datasets. These benchmark datasets contain a variety of multivariate IoT attacks. The aim is to demonstrate the robustness of the proposed algorithms in effectively identifying telnet, password, distributed denial of service (DDoS), injection, and backdoor vulnerabilities in IoT ecosystems. We achieved approximately 99.6% accuracy in correctly distinguishing between malevolent and non-malicious activities on the Kitsune dataset. Additionally, the TON-IoT dataset demonstrated a remarkable accuracy rate of 99.00%, with minimal drops and low false alert rates. The time efficiency of both proposed algorithms renders them well-suited for deployment in IoT ecosystems. We evaluated and cross validated the proposed techniques with current benchmarks. Consequently, the proposed hybrid deep learning anomaly detection approaches not only enhance IoT security but also provide a robust control system for addressing emerging multivariate cyber threats.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3482005" target="_blank">https://dx.doi.org/10.1109/access.2024.3482005</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_0b10c5b7febdea13fa2a9c19a6ca7aa6 |
| identifier_str_mv | 10.1109/access.2024.3482005 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/28442045 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Empowering IoT Resilience: Hybrid Deep Learning Techniques for Enhanced SecurityMuhammad Maaz (5600600)Ghufran Ahmed (6298196)Ahmad Sami Al-Shamayleh (17122985)Adnan Akhunzada (20151648)Shahbaz Siddiqui (6296942)Abdulla Hussein Al-Ghushami (20748818)Information and computing sciencesArtificial intelligenceCybersecurity and privacyData management and data scienceDistributed computing and systems softwareMachine learningIoTmachine learning (ML)deep learning (DL)cybersecurityDDOSinjection attacksbackdoorbotnet<p dir="ltr">The Internet of Things (IoT) has dramatically changed human context with the environment, ensuring productivity, comfort, and quality of life through a variety of services and applications. Nevertheless, the rapid growth of IoT devices has introduced significant security concerns like device vulnerabilities, unauthorized access, and potential data breaches.This article deals with an immediate call to empower IoT resilience against a wide range of sophisticated and prevalent cybersecurity threats. We developed two novel hybrid deep learning mechanisms, CNN-GRU (Convolutional Gated Recurrent Neural Networks) and CNN-LSTM (Convolutional Long Short-Term Memory Neural Networks), and extensively evaluated their performance on the state-of-the-art Kitsune and TON-IoT publicly available datasets. These benchmark datasets contain a variety of multivariate IoT attacks. The aim is to demonstrate the robustness of the proposed algorithms in effectively identifying telnet, password, distributed denial of service (DDoS), injection, and backdoor vulnerabilities in IoT ecosystems. We achieved approximately 99.6% accuracy in correctly distinguishing between malevolent and non-malicious activities on the Kitsune dataset. Additionally, the TON-IoT dataset demonstrated a remarkable accuracy rate of 99.00%, with minimal drops and low false alert rates. The time efficiency of both proposed algorithms renders them well-suited for deployment in IoT ecosystems. We evaluated and cross validated the proposed techniques with current benchmarks. Consequently, the proposed hybrid deep learning anomaly detection approaches not only enhance IoT security but also provide a robust control system for addressing emerging multivariate cyber threats.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3482005" target="_blank">https://dx.doi.org/10.1109/access.2024.3482005</a></p>2024-10-16T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2024.3482005https://figshare.com/articles/journal_contribution/Empowering_IoT_Resilience_Hybrid_Deep_Learning_Techniques_for_Enhanced_Security/28442045CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/284420452024-10-16T03:00:00Z |
| spellingShingle | Empowering IoT Resilience: Hybrid Deep Learning Techniques for Enhanced Security Muhammad Maaz (5600600) Information and computing sciences Artificial intelligence Cybersecurity and privacy Data management and data science Distributed computing and systems software Machine learning IoT machine learning (ML) deep learning (DL) cybersecurity DDOS injection attacks backdoor botnet |
| status_str | publishedVersion |
| title | Empowering IoT Resilience: Hybrid Deep Learning Techniques for Enhanced Security |
| title_full | Empowering IoT Resilience: Hybrid Deep Learning Techniques for Enhanced Security |
| title_fullStr | Empowering IoT Resilience: Hybrid Deep Learning Techniques for Enhanced Security |
| title_full_unstemmed | Empowering IoT Resilience: Hybrid Deep Learning Techniques for Enhanced Security |
| title_short | Empowering IoT Resilience: Hybrid Deep Learning Techniques for Enhanced Security |
| title_sort | Empowering IoT Resilience: Hybrid Deep Learning Techniques for Enhanced Security |
| topic | Information and computing sciences Artificial intelligence Cybersecurity and privacy Data management and data science Distributed computing and systems software Machine learning IoT machine learning (ML) deep learning (DL) cybersecurity DDOS injection attacks backdoor botnet |